Because latent trait models require that large numbers of items be calibrated or that testing of the same large group be repeated, item parameter estimates are often obtained by administering separate tests to different groups and "linking" the results to construct an adequate item pool. Four issues were studied, based upon the analysis of a realistic application of several linking procedures: (1) required sample size; (2) number of items overlapping between linked tests to allow accurate translation of scale; (3) effect of linking on quality of existing item parameter estimates; and (4) best linking procedure for the two latent trait models studied (one-parameter and three-parameter logistic models). The linking techniques used were the major axis method; least squares; least squares with outliers deleted; and maximum likelihood. Three different calibration programs were used. Data were achievement test scores for 4,000 high school students on the 357-item Iowa Tests of Educational Development (ITED). Results of the analyses indicated that no method was clearly superior. The more simple one-parameter logistic model was found to be more advantageous as far as required sample size. (GDC)